Sex on the Screen: A Content Analysis of Free Internet Pornography Depicting Mixed-Sex Threesomes from 2012–2020
Bibliographic record
Abstract
Viewing online pornography is common among US adults, with mixed-sex threesome (MST) videos being one of the top 10 most popular categories of pornography for both men and women. The current content analysis applied sexual script theory to understand the themes present in these mixed-sex threesome videos. Independent coders viewed a total of 50 videos (25 MMF and 25 FFM) at each timepoint (2012, 2015, 2020) and coded for different sexual behaviors and themes in each video. By examining both same-sex (female-female, male-male) and other-sex (female-male) behaviors, as well as themes of aggression and sexual initiation in different videos and across three timepoints, it was determined that other-sex behaviors are more common in MST videos than same-sex behaviors. Same-sex behaviors between two female actors were more common than same-sex behaviors between two male actors. Aggression was a common theme in videos, with male actors being more aggressive on average than female actors. Most of these trends did not change across 8 years, suggesting that the impacts of traditional sexual scripts are pervasive in pornography, even in current online content. Important implications for both researchers and clinical professionals are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".